Endogeneity issues
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Description
Hi all and thanks for the nice work you are doing.
I am working on a panel database, and would like to have info about how the algorithm deals with endogeneity in a panel data structure. In particular, I have applied the algorithm with your help using SparseCATE routine, debiased inference and accounting also for time, group and individual fixed effects. My question is whther the method considers the possibility of a relationship between features/treatment and possible unobservables.
Checking the online documentation I have seen that there is a Z called instrument. How is it employed in thee algorithm? Do I need to use it or I have already taken into account possible endogeneity by using the mentioned routines?
Thank you in advance,
Federico
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Research direction
Start with the online documentation for SparseCATE, debiased inference, fixed effects, and the instrument argument. The issue does not name a file or implementation target; it needs a maintainer-level explanation of how endogeneity is handled and when the instrument should be used before any documentation change can be defined.
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Assessment
- Tech stack
- python
- Domain
- documentation, machine-learning
- Issue type
- Documentation
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100